Title

Author

Date

July 2013

Document Type

Thesis

Degree Name

M.P.H.

Department

Dept. of Public Health and Preventive Medicine

Institution

Oregon Health & Science University

Abstract

Microarray experiments allow researchers to assess the levels of gene expression for tens of thousands of genes at a time. A frequent goal of microarray experiments is to identify genes which are differentially expressed across various biological conditions. Several methods have been developed for determining sample size for differential expression microarray experiments, but few methods have been extended to time course experiments in which gene expression is measured over a series of time points. This thesis proposes a flexible method for sample size and power analysis of time course microarray experiments using a positive false discovery rate type I error control. Because microarray data is often observed to deviate from the assumption of normality underlying the use of parametric t-tests and F-tests, and since it has been increasingly recognized that accounting for the correlation structure of gene expression data is important for accurately estimating error rate and sample size, t

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